Investigating trends in process error as a diagnostic for integrated fisheries stock assessments
Investigating trends in process error as a diagnostic for integrated fisheries stock assessments
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研究过程错误的趋势作为综合渔业资源评估的诊断
DOI：
10.1016/j.fishres.2022.106478
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发表时间：
2022
影响因子：
2.4
通讯作者：
Santiag
中科院分区：
文献类型：
--
作者：
Merino Gorka;Urtizberea Agurtzane;Fu Dan;Winker Henning;Cardinale Massimiliano;Lauretta Matthew V.;Murua Hilario;Kitakado Toshihide;Arrizabalaga Haritz;Scott Robert;Pilling Graham;Minte-Vera Carolina;Xu Haikun;Laborda Ane;Erauskin-Extramiana Maite;Santiag
综合种群评估包括拟合多个渔获量来源、丰度和辅助生物信息，以估计描述鱼类种群动态的方程参数。种群评估存在不确定性，通常的做法是在模型集合中使用替代假设和假设来描述不确定性，以制定渔业管理的科学建议。在这种情况下，需要为最终反映不同生物和渔业过程的不确定性的每种因素组合分配合理性水平。在本研究中，我们描述并应用模型诊断来识别热带金枪鱼综合评估模型集合中招募偏差估计的过程误差趋势。我们证明，热带金枪鱼的评估模型集合包含不同的情景，这些情景具有被忽视的过程错误的显着趋势，以及对渔业管理的相关影响。以印度洋黄鳍金枪鱼作为案例研究，我们发现补充偏差的趋势与极端生产力情景有关，这些情景在规模上与没有补充偏差的确定性模型存在很大差异。这表明，当补充偏差呈现增加趋势时，这些可以补偿高捕捞期间超出剩余产量的生物量损失。在这些情况下，招聘的变化不是一个随机过程，而是发挥了生产力的补偿性、系统性驱动作用。招募的显着趋势与标准差和自相关系数的增加、与丰度指数拟合的非随机残差模式以及特别是年龄结构生产模型（ASPM）诊断的较差表现呈正相关。我们认为，招募偏差的趋势可能是由于在综合评估模型中用作固定值的生物参数的错误指定造成的。当使用模型集合来制定渔业管理建议时，此处描述的过程错误诊断可以提供统计标准来支持假设和假设。
Integrated stock assessments consist of fitting several sources of catch, abundance, and auxiliary biological information to estimate parameters of equations that describe the population dynamics of fish stocks. Stock assessments are subject to uncertainty, and it is a common practice to characterize uncertainty using alternative hypotheses and assumptions within an ensemble of models to develop scientific advice for fisheries management. In this context, there is the need to assign levels of plausibility to each of the combinations of factors that ultimately reflect the uncertainty on different biological and fishery processes. In this study, we describe and apply a model diagnostic to identify trends in process error in recruitment deviation estimates within ensembles of integrated assessment models of tropical tunas. We demonstrate that assessment model ensembles for tropical tunas contain distinct scenarios with significant trends in process error that are overlooked, with the associated implications for fisheries management. Using the Indian Ocean yellowfin as a case study, we found that trends in recruitment deviates are linked to extreme productivity scenarios which strongly diverged in scale from deterministic models fitted without recruitment deviates. This indicates that when recruitment deviates show an increasing trend, these can compensate for the loss of biomass in periods of high catch beyond the surplus production. In these cases, variation in recruitment is not a random process, but rather takes the function of a compensatory, systematic driver in productivity. Significant trends in recruitment were positively correlated with increased standard deviations and auto-correlation coefficient, non-random residual pattern in fits to abundance indices, and particularly poor performance of the Age-Structured Production Model (ASPM) diagnostic. We suggest that trends in recruitment deviates can be caused by misspecification of the biological parameters used as fixed values in integrated assessment models. The process error diagnostic described here can provide a statistical criterion in support for hypotheses and assumptions when using ensembles of models to develop fisheries management advice.